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Latency-Aware Collaborative Perception
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The available bandwidth for communication between vehicles and infrastructure is often limited, and sharing this limited resource among numerous vehicles can lead to reduced data transfer rates and slower communication.
“Moreover, the available bandwidth for communication between vehicles and infrastructure is often limited.Sharing this limited resource among numerous vehicles can lead to reduced data transfer rates and slower communication.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [34]
In collaborative perception, latency issues can be caused by communication delay between multiple agents, and this delay can cause potential performance degradation and high risks in safety-critical applications.
“In the context of collaborative perception, latency issues can be caused by the communication delay between multiple agents.This delay can cause potential performance degradation and high risks in safety-critical applications [34].”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [34]
Network congestion and limited bandwidth can cause latency in transmitting data, impacting the real-time nature of ICV communication.
“Furthermore, network congestion and limited bandwidth can cause latency in transmitting data, impacting the real-time nature of ICV communication.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [34]
A latency-aware collaborative perception system leverages feature-attention symbiotic estimation and time modulation techniques to achieve feature-level synchronization.
“To further improve the generalization ability in real-world scenarios, a latency-aware collaborative perception system is proposed [34], which leverages feature-attention symbiotic estimation and time modulation techniques to achieve feature-level synchronization.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [34]
A curriculum learning technology is used to gradually increase the latency time by randomly sampling the latency time with an exponential distribution to accommodate the flexible communication latency.
“Furthermore, a curriculum learning technology is used to gradually increase the latency time by randomly sampling the latency time with an exponential distribution to accommodate the flexible communication latency.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [34]
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References 32 · Cited by 147
Research works printed but not linked: 35
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Metadata
- Authors
- Lei, Z., Ren, S., Hu, Y., Zhang, W., & Chen, S.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Chen, Siheng
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 316-332
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312571135
- Cited by
- 147
- References
- 32
- Citations by year
- 2026: 30 · 2025: 53 · 2024: 38 · 2023: 25 · 2022: 1
- FWCI
- 30.04
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (21511100900, 62171276, 2021ZD0112801, 2021-01)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- cooperative perception, communication delay, latency compensation, feature attention, temporal modulation, autonomous driving, multi-agent perception, safety-critical systems
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